Sekai: A Video Dataset towards World Exploration

Zhen Li, Chuanhao Li, Xiaofeng Mao, Shaoheng Lin, Ming Li, Shitian Zhao, xu Zhao Pan, Xinyue Li, Yukang Feng, Jianwen Sun, Zizhen Li, Fanrui Zhang, Jiaxin Ai, Zhixiang Wang, Yuwei Wu, Tong He, Yunde Jia, Kaipeng Zhang

Keywords: Video Generation, Long Video Generation, Interactive Video Generation, Controllable Video Generation

Abstract:

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration.
However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world.
In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration.
It consists of over 5,000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories.
Comprehensive analyses and experiments demonstrate the dataset’s scale, diversity, annotation quality, and effectiveness for training video generation models.
We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

Dataset URL: https://huggingface.co/datasets/Lixsp11/Sekai-Project

Code URL: https://github.com/Lixsp11/sekai-codebase

Primary Area: Applications of Datasets & Benchmarks for in Creative AI

Flagged For Ethics Review: true

Submission Number: 309